





"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Optional[Any]						    =   DiTPipeline
       __magic_name__	:Optional[Any]						    =   CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
       __magic_name__	:Dict						    =   PipelineTesterMixin.required_optional_params - {
           """latents""",
           """num_images_per_prompt""",
           """callback""",
           """callback_steps""",
       }
       __magic_name__	:List[Any]						    =   CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
       __magic_name__	:Dict						    =   False






       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.manual_seed(0 )
         lowerCAmelCase__     :str											=				TransformeraDModel(
             sample_size=1_6       ,					num_layers=2       ,					patch_size=4       ,					attention_head_dim=8       ,					num_attention_heads=2       ,					in_channels=4       ,					out_channels=8       ,					attention_bias=__UpperCAmelCase       ,					activation_fn='gelu-approximate'       ,					num_embeds_ada_norm=1_0_0_0       ,					norm_type='ada_norm_zero'       ,					norm_elementwise_affine=__UpperCAmelCase       ,					)
         lowerCAmelCase__     :int											=				AutoencoderKL()
         lowerCAmelCase__     :Union[str, Any]											=				DDIMScheduler()
         lowerCAmelCase__     :List[Any]											=				{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
         return components






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         if str(__UpperCAmelCase ).startswith('mps' ):
           lowerCAmelCase__     :int											=				torch.manual_seed(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :List[str]											=				torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[int]											=				{
             'class_labels': [1],
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				'cpu'

         lowerCAmelCase__     :str											=				self.get_dummy_components()
         lowerCAmelCase__     :Any											=				self.pipeline_class(**__UpperCAmelCase )
         pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Tuple											=				self.get_dummy_inputs(__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Optional[int]											=				image[0, -3:, -3:, -1]

         self.assertEqual(image.shape       ,					(1, 1_6, 1_6, 3) )
         lowerCAmelCase__     :List[str]											=				np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57] )
         lowerCAmelCase__     :Optional[int]											=				np.abs(image_slice.flatten() - expected_slice ).max()
         self.assertLessEqual(__UpperCAmelCase       ,					1E-3 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self._test_inference_batch_single_identical(relax_max_difference=__UpperCAmelCase       ,					expected_max_diff=1E-3 )






       @unittest.skipIf(
           torch_device != 'cuda' or not is_xformers_available()       ,					reason='XFormers attention is only available with CUDA and `xformers` installed'       ,					)
       def 	snake_case		(       self ):



         '''simple docstring'''

         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )






@require_torch_gpu
@slow
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				torch.manual_seed(0 )

         lowerCAmelCase__     :Optional[int]											=				DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
         pipe.to('cuda' )

         lowerCAmelCase__     :Union[str, Any]											=				['vase', 'umbrella', 'white shark', 'white wolf']
         lowerCAmelCase__     :Tuple											=				pipe.get_label_ids(__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				pipe(__UpperCAmelCase       ,					generator=__UpperCAmelCase       ,					num_inference_steps=4_0       ,					output_type='np' ).images

         for word, image in zip(__UpperCAmelCase       ,					__UpperCAmelCase ):
           lowerCAmelCase__     :Optional[Any]											=				load_numpy(
               F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy" )
           assert np.abs((expected_image - image).max() ) < 1E-2






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
         lowerCAmelCase__     :Optional[Any]											=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
         pipe.to('cuda' )

         lowerCAmelCase__     :Optional[int]											=				['vase', 'umbrella']
         lowerCAmelCase__     :Optional[Any]											=				pipe.get_label_ids(__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				torch.manual_seed(0 )
         lowerCAmelCase__     :int											=				pipe(__UpperCAmelCase       ,					generator=__UpperCAmelCase       ,					num_inference_steps=2_5       ,					output_type='np' ).images

         for word, image in zip(__UpperCAmelCase       ,					__UpperCAmelCase ):
           lowerCAmelCase__     :Optional[int]											=				load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               F"/dit/{word}_512.npy" )

           assert np.abs((expected_image - image).max() ) < 1E-1




from __future__ import annotations

import math
import random
from typing import Any







class 							lowerCamelCase__						:






			"""simple docstring"""






			def __init__(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							[]
						UpperCamelCase__       =							0
						UpperCamelCase__       =							0




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.head == self.tail




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						self.data.append(snake_case  )
						UpperCamelCase__       =							self.tail + 1




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.data[self.head]
						UpperCamelCase__       =							self.head + 1
						return ret




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.tail - self.head







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						print(self.data  )
						print("**************"  )
						print(self.data[self.head : self.tail]  )







class 							lowerCamelCase__						:






			"""simple docstring"""






			def __init__(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							data
						UpperCamelCase__       =							None
						UpperCamelCase__       =							None
						UpperCamelCase__       =							1




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.data




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.left




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.right




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.height




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							data




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							node




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							node







			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							height



def 							UpperCamelCase_(					_A :MyNode | None	)->		int:
			if node is None:
						return 0
			return node.get_height()
def 							UpperCamelCase_(					_A :int					,  _A :int	)->		int:
			if a > b:
						return a
			return b
def 							UpperCamelCase_(					_A :MyNode	)->		MyNode:
			print("left rotation node:"					,  node.get_data()	)
			UpperCamelCase__       =							node.get_left()
			assert ret is not None
			node.set_left(ret.get_right()	)
			ret.set_right(_A	)
			UpperCamelCase__       =							my_max(get_height(node.get_right()	)					,  get_height(node.get_left()	)	) + 1
			node.set_height(_A	)
			UpperCamelCase__       =							my_max(get_height(ret.get_right()	)					,  get_height(ret.get_left()	)	) + 1
			ret.set_height(_A	)
			return ret
def 							UpperCamelCase_(					_A :MyNode	)->		MyNode:
			print("right rotation node:"					,  node.get_data()	)
			UpperCamelCase__       =							node.get_right()
			assert ret is not None
			node.set_right(ret.get_left()	)
			ret.set_left(_A	)
			UpperCamelCase__       =							my_max(get_height(node.get_right()	)					,  get_height(node.get_left()	)	) + 1
			node.set_height(_A	)
			UpperCamelCase__       =							my_max(get_height(ret.get_right()	)					,  get_height(ret.get_left()	)	) + 1
			ret.set_height(_A	)
			return ret
def 							UpperCamelCase_(					_A :MyNode	)->		MyNode:
			UpperCamelCase__       =							node.get_left()
			assert left_child is not None
			node.set_left(left_rotation(_A	)	)
			return right_rotation(_A	)
def 							UpperCamelCase_(					_A :MyNode	)->		MyNode:
			UpperCamelCase__       =							node.get_right()
			assert right_child is not None
			node.set_right(right_rotation(_A	)	)
			return left_rotation(_A	)
def 							UpperCamelCase_(					_A :MyNode | None					,  _A :Any	)->		MyNode | None:
			if node is None:
						return MyNode(_A	)
			if data < node.get_data():
						node.set_left(insert_node(node.get_left()					,  _A	)	)
						if (
						    get_height(node.get_left()	) - get_height(node.get_right()	) == 2
						):  # an unbalance detected
									UpperCamelCase__       =							node.get_left()
									assert left_child is not None
									if (
									    data < left_child.get_data()
									):  # new node is the left child of the left child
												UpperCamelCase__       =							right_rotation(_A	)
									else:
												UpperCamelCase__       =							lr_rotation(_A	)
			else:
						node.set_right(insert_node(node.get_right()					,  _A	)	)
						if get_height(node.get_right()	) - get_height(node.get_left()	) == 2:
									UpperCamelCase__       =							node.get_right()
									assert right_child is not None
									if data < right_child.get_data():
												UpperCamelCase__       =							rl_rotation(_A	)
									else:
												UpperCamelCase__       =							left_rotation(_A	)
			UpperCamelCase__       =							my_max(get_height(node.get_right()	)					,  get_height(node.get_left()	)	) + 1
			node.set_height(_A	)
			return node
def 							UpperCamelCase_(					_A :MyNode	)->		Any:
			while True:
						UpperCamelCase__       =							root.get_right()
						if right_child is None:
									break
						UpperCamelCase__       =							right_child
			return root.get_data()
def 							UpperCamelCase_(					_A :MyNode	)->		Any:
			while True:
						UpperCamelCase__       =							root.get_left()
						if left_child is None:
									break
						UpperCamelCase__       =							left_child
			return root.get_data()
def 							UpperCamelCase_(					_A :MyNode					,  _A :Any	)->		MyNode | None:
			UpperCamelCase__       =							root.get_left()
			UpperCamelCase__       =							root.get_right()
			if root.get_data() == data:
						if left_child is not None and right_child is not None:
									UpperCamelCase__       =							get_left_most(_A	)
									root.set_data(_A	)
									root.set_right(del_node(_A					,  _A	)	)
						elif left_child is not None:
									UpperCamelCase__       =							left_child
						elif right_child is not None:
									UpperCamelCase__       =							right_child
						else:
									return None
			elif root.get_data() > data:
						if left_child is None:
									print("No such data"	)
									return root
						else:
									root.set_left(del_node(_A					,  _A	)	)
			else:  # root.get_data() < data
						if right_child is None:
									return root
						else:
									root.set_right(del_node(_A					,  _A	)	)

			if get_height(_A	) - get_height(_A	) == 2:
						assert right_child is not None
						if get_height(right_child.get_right()	) > get_height(right_child.get_left()	):
									UpperCamelCase__       =							left_rotation(_A	)
						else:
									UpperCamelCase__       =							rl_rotation(_A	)
			elif get_height(_A	) - get_height(_A	) == -2:
						assert left_child is not None
						if get_height(left_child.get_left()	) > get_height(left_child.get_right()	):
									UpperCamelCase__       =							right_rotation(_A	)
						else:
									UpperCamelCase__       =							lr_rotation(_A	)
			UpperCamelCase__       =							my_max(get_height(root.get_right()	)					,  get_height(root.get_left()	)	) + 1
			root.set_height(_A	)
			return root







class 							lowerCamelCase__						:






			"""simple docstring"""






			def __init__(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							None




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return get_height(self.root  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						print("insert:" + str(snake_case  )  )
						UpperCamelCase__       =							insert_node(self.root   ,    snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						print("delete:" + str(snake_case  )  )
						if self.root is None:
									print("Tree is empty!"  )
									return
						UpperCamelCase__       =							del_node(self.root   ,    snake_case  )







			def __str__(				self   ,    ):  # a level traversale, gives a more intuitive look on the tree

						'''simple docstring'''


						UpperCamelCase__       =							""
						UpperCamelCase__       =							MyQueue()
						q.push(self.root  )
						UpperCamelCase__       =							self.get_height()
						if layer == 0:
									return output
						UpperCamelCase__       =							0
						while not q.is_empty():
									UpperCamelCase__       =							q.pop()
									UpperCamelCase__       =							" " * int(math.pow(2   ,    layer - 1  )  )
									output += space
									if node is None:
												output += "*"
												q.push(snake_case  )
												q.push(snake_case  )
									else:
												output += str(node.get_data()  )
												q.push(node.get_left()  )
												q.push(node.get_right()  )
									output += space
									UpperCamelCase__       =							cnt + 1
									for i in range(100  ):
												if cnt == math.pow(2   ,    snake_case  ) - 1:
															UpperCamelCase__       =							layer - 1
															if layer == 0:
																		output += "\n*************************************"
																		return output
															output += "\n"
															break
						output += "\n*************************************"
						return output



def 							UpperCamelCase_(					)->		None:
			import doctest

			doctest.testmod()


if __name__ == "__main__":
						_test()
						__UpperCamelCase     		=							AVLtree()
						__UpperCamelCase     		=							list(range(1_0))
						random.shuffle(lst)
						for i in lst:
												t.insert(i)
												print(str(t))
						random.shuffle(lst)
						for i in lst:
												t.del_node(i)
												print(str(t))


